Operations Data Engineer

Build the data foundation Operations relies on by designing and delivering modern, scalable infrastructure that powers analytics, machine learning, and AI-driven decision-making. This onsite role in Austin, TX focuses on end-to-end pipelines, data modeling, orchestration with monitoring and SLA management, and production AIML workflows. You will partner with teams across analytics and engineering to translate data needs into reliable data products and help operationalize models in production.

What you’ll do

• Work with business and analytics stakeholders to understand operational data requirements and deliver robust, scalable solutions that influence Operations decisions.
• Design and implement end-to-end data pipelines and architectures across batch and real-time streaming workloads, from ingestion and transformation to delivery.
• Develop and maintain high-quality data models (dimensional, relational, or knowledge graph-based) using dbt to support analytics and AIML use cases at scale.
• Architect and run data workflows with orchestration tools such as Apache Airflow, including built-in monitoring, alerting, and SLA management.
• Implement data observability, lineage tracking, and validation frameworks to protect data integrity and strengthen trust in downstream results.
• Collaborate with Data Scientists, ML Engineers, Software Engineers, and Analysts to operationalize models and ensure the platform supports production AIML workflows.
• Partner with infrastructure and platform teams on cloud-native data environments using Snowflake, Spark, and lakehouse technologies such as Delta Lake and Apache Iceberg, with attention to performance, cost efficiency, and scalability.
• Use AI-assisted development tools (for example, GitHub and Claude) and LLM-powered agents to accelerate pipeline authoring, code review, documentation, and transformation logic from natural language specifications.
• Apply DataOps practices with CI/CD, version control, automated testing, and containerization using Docker and Kubernetes to deliver production-grade data products.
• Adopt a data product mindset to enable self-serve analytics and reduce bottlenecks for downstream consumers.
• Tune query performance through partitioning strategies and storage optimization for cloud warehouses and lakehouses.
• Maintain clear technical documentation including data dictionaries, lineage diagrams, and architecture decision records.
• Share infrastructure capabilities, health metrics, and architectural recommendations with senior leadership in clear, non-technical terms.
• Research and evaluate emerging data engineering technologies, including streaming architectures, GenAI-powered data tooling, and next-generation warehousing, to expand team capabilities.

What you’ll bring

• MS in Computer Science, Data Engineering, Statistics, Applied Math, Data Science, Operations Research, or a related field and 8+ years of industry experience, OR BS in a related field with 10+ years hands-on industry experience.
• Domain expertise in supply chain, operations management, logistics, planning & forecasting, production integration, and channel management.
• Demonstrated expertise building and operating large-scale ETL/ELT pipelines using Python, SQL, and modern frameworks such as dbt, Spark, and Kafka/Flink for streaming.
• Proficiency with cloud data platforms (e.g., Snowflake) and open table formats (Delta Lake, Apache Iceberg).
• Strong command of advanced SQL for complex data modeling, query optimization, and analytics engineering.
• Experience with workflow orchestration tools (such as Apache Airflow or equivalent) and building production-grade, monitored pipelines.
• Hands-on experience implementing data quality frameworks, observability tooling, and data lineage tracking in production environments.
• Experience productionalizing GenAI and Agentic AI tooling, including LLM-assisted code generation, MCP servers, and AI-powered data pipeline automation.
• Experience with data visualization and self-serve analytics platforms (e.g., Tableau, Streamlit, ThoughtSpot) and the ability to build light front-end data products.
• Track record of staying current with best practices, adopting emerging technologies (such as vector databases, RAG pipelines, and AI-native data tools), and building functional prototypes to validate concepts.

Technologies you’ll work with

Python, SQL, dbt, Spark, Kafka, Flink, Snowflake, Delta Lake, Apache Iceberg, Apache Airflow, GitHub, Claude, LLM-powered agents, Docker, Kubernetes, Tableau, Streamlit, ThoughtSpot, MCP servers, vector databases, RAG pipelines

Preferred qualifications

• Ability to work in a fast-paced, iterative environment and deliver under timeline pressure.
• Champion experimentation and continuous learning across reporting, business analytics, and AI-powered automation.
• Exceptional ability to explain complex data architecture decisions to both technical peers and non-technical senior stakeholders.
• Strong collaboration skills to partner across functions, share knowledge, and integrate diverse feedback.
• Self-sufficient with the ability to thrive with autonomy amid ambiguity, with a high bias for action and careful attention to data integrity.